UAV Path Planning Using on-Board Ultrasound Transducer Arrays and Edge Support

Figetakis, Emanuel; Refaey, Ahmed

doi:10.1109/iccworkshops50388.2021.9473500

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Different metrics are selected based on the type of problem, i.e., classification or regression. Some of the widely used loss functions to capture the learning of the DL model at the edge while training are mean absolute error [204,205,206], mean square error [207,208], negative log-likelihood [209,210], cross-entropy [211,212,213], Kullback-Leibler divergence [214,215,216] etc. Cross-entropy, also called logarithmic loss, log loss, or logistic loss, is a widely accepted loss function for classification problems.…”

Section: B Training Lossmentioning

confidence: 99%

Enabling All In-Edge Deep Learning: A Literature Review

et al. 2023

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In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition, image processing, and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers (a mesh of computing devices near end devices). Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). International Electrotechnical Commission (IEC) defines EI as the concept where the data is acquired, stored, and processed utilizing edge computing with DL and advanced networking capabilities. Broadly, EI has six levels of categories based on where the training and inference of DL take place, e.g., cloud server, edge server and end devices. This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important such as in mission-critical applications (e.g., health care). Besides, 5G/6G networks are envisioned to use all in-edge. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented. INDEX TERMS Artificial intelligence, all in-edge, deep learning, distributed systems, decentralized systems, edge intelligence I. INTRODUCTION T HE global community is increasingly becoming a datadriven environment in which end devices are generating vast quantities of data outside of the traditional data centers. International Telecommunication Union anticipates that global internet traffic per month will reach 607 Exabytes (EB) in 2025 and 5016 EB in 2030 [1]. This enormous amount of data has a positive impact on artificial intelligence (AI) applications. In particular, deep learning (DL) rely on the availability of large quantities of data for its d...

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Section: B Training Lossmentioning

confidence: 99%

Enabling All In-Edge Deep Learning: A Literature Review

et al. 2023

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“…These studies include obstacle avoidance based on ultrasonic, radar, and image processing [ 13 ]. Ultrasonic-based methods perform in real-time but the maximum range is short [ 14 , 15 ]. Radar-based methods perform well in obstacle detection.…”

Section: Related Workmentioning

confidence: 99%

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Shimada

Nishikawa

Kong

et al. 2022

Sensors

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In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work.

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“…Different metrics are selected based on the type of problem, i.e., classification or regression. Some of the widely used loss functions to capture the learning of DNN at EDGE while training are Mean Absolute Error [60,64,273], Mean Square Error Loss [269,289,316], Negative Log-Likelihood Loss [138,147,219], Cross-Entropy Loss [54,57,128,162], Kullback-Leibler divergence [47,70,237,294] etc.…”

Section: Training Lossmentioning

confidence: 99%

Enabling Deep Learning for All-in EDGE paradigm

Joshi¹,

Hasanuzzaman²,

Thapa³

et al. 2022

Preprint

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Deep Learning-based models have been widely investigated, and they have demonstrated significant performance on non-trivial tasks such as speech recognition, image processing, and natural language understanding. However, this is at the cost of substantial data requirements. Considering the widespread proliferation of edge devices (e.g., Internet of Things devices) over the last decade, Deep Learning in the edge paradigm, such as device-cloud integrated platforms, is required to leverage its superior performance. Moreover, it is suitable from the data requirements perspective in the edge paradigm because the proliferation of edge devices has resulted in an explosion in the volume of generated and collected data. However, there are difficulties due to other requirements such as high computation, high latency, and high bandwidth caused by Deep Learning applications in real-world scenarios. In this regard, this survey paper investigates Deep Learning at the edge, its architecture, enabling technologies, and model adaption techniques, where edge servers and edge devices participate in deep learning training and inference. For simplicity, we call this paradigm the All-in EDGE paradigm. Besides, this paper presents the key performance metrics for Deep Learning at the All-in EDGE paradigm to evaluate various deep learning techniques and choose a suitable design. Moreover, various open challenges arising from the deployment of Deep Learning at the All-in EDGE paradigm are identified and discussed.

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UAV Path Planning Using on-Board Ultrasound Transducer Arrays and Edge Support

Cited by 5 publications

References 12 publications

Enabling All In-Edge Deep Learning: A Literature Review

Enabling All In-Edge Deep Learning: A Literature Review

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Enabling Deep Learning for All-in EDGE paradigm

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